Scent Explore invites you to discover your environment in an exciting way. Little Scent Explore characters are hiding in interesting places around you, and you collect points when you find them! Your smartphone or tablet will give an audio alert when you are approaching a character. To earn points, just tap on the screen to capture a character when you see it. Your camera will take a photo of the location and upload it to Scent. Compete with your friends and family and see who wins!

By playing Scent Explore, you make a valuable contribution to citizen science. You help researchers understand changes in the environment because all the locations in which you catch Scent characters contain interesting information that researchers will use to analyse and map the environment.

Join the Scent movement and become a citizen scientist today!

The app uses portable sensors to measure and report the soil moisture and air temperature to your smartphone or tablet.

Scent Measure is easy to use – simply insert the portable sensor into the soil, select whether to measure temperature or moisture, and receive the measures directly to your screen.

Scent Measure is triggered from Scent Explore app. When playing Scent Explore, you will be prompted to take a measurement at an important point of interest for scientists and researchers, and thereby contribute to monitoring and analysing your environment.

 

SCENT Measure application is released under an open source license and is available here

It acts as a central data broker that links the Scent frontend applications used by citizens to provide images, annotations, sensory data, event reports and videos, to all the other toolbox components.

The crowdsourcing platform allows local authorities to configure and manage crowdsourcing campaigns, define points of interest where pictures and measurements are needed, and monitor in real-time the pictures collected by citizens and the issues reported.

It also manages the complete lifecycle of citizen-generated images. Once a citizen takes a relevant picture using the Scent Explore app, the picture is sent to the crowdsourcing platform. It stores a copy of the image on the cloud and makes it available to the Scent artificial intelligence ‘heart’, also known as the Scent Intelligence Engine (SIE), which uses advanced machine learning techniques to automatically understand what is in the image. The annotations are fed back to the crowdsourcing platform, which is in charge of deciding whether the quality of the annotations is sufficient. If not, the image is made available for being further annotated, this time by people, through the CAPTCHA plugin or the Scent Collaborate app.

 

SCENT Measure application is released under an open source license and is available here.

The models are computational representations of water dynamics in the pilot areas, and they are initially built using existing ground observations and remote sensing data.

After the Scent field campaigns, the models will be enhanced with crowdsourced data. The value of citizens’ contributions and their role in generating improved flood risk maps and spatiotemporal flooding patterns will be assessed.

The different characteristics of the two areas require different modelling approaches. In the Danube Delta, specifically in the Sontea-Fortuna pilot area, two hydrodynamic models were set up in order to simulate the complex network of channels and lakes in the area: one 1D model and one 1D/2D model.

In the Kifisos catchment, the pilot area is the upstream region of Athens city, where many hydrological components of the water cycle are more influent. A combination of models was therefore set up: a hydrological model connected to a hydrodynamic model at the end of the basin.

These models form a key component of the Toolbox as they serve as a basis for determining how they can be enriched through crowd-sourced data.

Citizen-generated data are utilized for the training of the map segmentation tool, that aims to produce LC/LU maps of improved accuracy which are essential for the construction of hydrodynamic and hydrological models. These models are used to produce flood risk and pattern maps for urban and rural flood cases.

Last but not least, there is a prominent need to include automatic assessment and monitoring of land cover and state of soil in rural and urban areas as well as to detect seasonal changes, natural disasters, and human-related area development. Thus, the map segmentation tool aims to extend current repositories (i.e. GEOSS) by allowing for more frequent updates of local monitoring of land cover/use changes and with higher spatial resolution, using inexpensive crowd-sourcing tools.